Head of Analytics, Chubb Life

Purpose

Build and lead Chubb Life's Analytics function globally - covering data science, predictive modelling, scoring, cognitive use case design, and business intelligence across 8 Asian markets. This is Chubb Life's chief analytical authority: designing the science and intelligence layer that the AI Engineering team operationalises into production. Building from scratch, this leader must combine academic rigour with the commercial drive to ship models that move real business outcomes.

Responsibilities

Data Science, Scoring and Predictive Modelling

• Develop production-grade ML and statistical models across core insurance domains: agent scoring, persistency and lapse prediction, customer lifetime value, cross-sell propensity, risk segmentation, and claims analytics.

• Define modelling methodology, feature engineering standards, and model validation frameworks used globally.

• Own the full model development lifecycle from hypothesis through training, validation, and structured handoff to AI Engineering for deployment.

 

Cognitive Analytics and GenAI Use Case Design

• Own the intelligence design layer for GenAI and cognitive AI applications - defining what the AI should know, reason about, and decide, with measurable success criteria.

• Partner with the Head of AI Engineering to translate cognitive use cases (agent coaching, sales advisory, customer stewardship) into well-specified engineering requirements.

• Continuously evaluate emerging ML/AI techniques and identify which are ready for production adoption in an insurance context.

 

Business Intelligence and Descriptive Analytics

• Govern the BI and descriptive analytics layer - ensuring business leaders have timely, trusted insights through PowerBI and Insights Studio.

• Define the analytics taxonomy: KPIs, metric definitions, and reporting standards across all business functions.

• Build self-serve analytics capability within a governed, scalable framework.

 

Cross-Functional Analytics

• Deliver analytics value across Distribution/Agency, Actuarial/Risk, Customer, and Operations.

• Translate business questions into scoped analytical problems with clear hypotheses and commercial success criteria.

• Ensure every model and insight connects directly to a measurable business outcome.

 

Team Building and Capability Development

• Build the Analytics function from scratch - define team structure, hiring profiles, and operating model for data scientists and ML practitioners.

• Foster a culture that balances academic depth with delivery velocity: research thinking and production discipline, coexisting.

• Mentor team members in statistical modelling, ML engineering, and applied AI.

 

Governance and Standards

• Define modelling conventions, peer review protocols, documentation standards, and model risk management practices.

• Ensure all models meet responsible AI principles: fairness, explainability, regulatory compliance, and data privacy.

• Maintain model inventories and governance documentation for audit and regulatory purposes.

• 12-15+ years in data science, advanced analytics, or quantitative research; 4+ years leading analytical teams.

• Insurance industry experience is critical.

• Deep expertise in statistical modelling and ML: supervised/unsupervised learning, ensemble methods, survival models, NLP, and deep learning.

• Production track record: models built, validated, and deployed at scale in insurance or financial services.

• Experience designing the analytical intelligence layer for GenAI/cognitive AI applications.

• Strong command of Python, scikit-learn, XGBoost, PyTorch, Databricks MLflow, and Snowflake.

• Proven ability to build analytics teams from scratch and develop talent pipelines.

• Communicates complex quantitative findings to non-technical executives with clarity and confidence.

• PhD or Master's in Statistics, Mathematics, Computer Science, Actuarial Science, or a related quantitative discipline strongly preferred.